Generalizing to Unseen Domains via Adversarial Data Augmentation (1805.12018v2)

Abstract: We are concerned with learning models that generalize well to different \emph{unseen} domains. We consider a worst-case formulation over data distributions that are near the source domain in the feature space. Only using training data from a single source distribution, we propose an iterative procedure that augments the dataset with examples from a fictitious target domain that is "hard" under the current model. We show that our iterative scheme is an adaptive data augmentation method where we append adversarial examples at each iteration. For softmax losses, we show that our method is a data-dependent regularization scheme that behaves differently from classical regularizers that regularize towards zero (e.g., ridge or lasso). On digit recognition and semantic segmentation tasks, our method learns models improve performance across a range of a priori unknown target domains.

• The paper introduces an iterative adversarial augmentation method that challenges models with synthetic hard examples to boost generalization to unseen domains.
• It employs softmax loss regularization that adapts to true label parameters, enhancing semantic consistency and robustness beyond conventional techniques.
• Empirical results on digit classification and semantic segmentation demonstrate significant performance gains over standard ERM and other baseline regularization methods.

Generalizing to Unseen Domains via Adversarial Data Augmentation

The paper "Generalizing to Unseen Domains via Adversarial Data Augmentation" by Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John Duchi, Vittorio Murino, and Silvio Savarese explores a methodology to improve model generalization to unseen domains. This is achieved by iteratively augmenting the training dataset with adversarial examples which reflect difficult or 'hard' instances under the current model. The approach aligns closely with paradigms such as adversarial training and distributionally robust optimization (DRO), but uniquely applies these principles to target out-of-distribution (OOD) scenarios without access to any data from these unseen domains during training.

Methodology and Key Concepts

The core idea is to formulate a worst-case optimization problem over data distributions in the feature space proximate to the source domain. The authors propose an iterative adversarial data augmentation technique wherein, during each iteration, the model is exposed to samples from a fictitious target domain constructed to be challenging under the current parameters.

Fundamentally, their adversarial framework is powered by two primary components:

1. Iterative Adversarial Augmentation: Here, the dataset is progressively augmented with adversarially created examples. Each augmentation step aims to generate data points that stretch the model's robustness, simulating potential covariate shifts, thereby compelling the model to generalize beyond the immediate training distribution.
2. Softmax Loss Regularization: For classifiers using softmax loss, the authors show that their method functions as a data-dependent regularizer. Rather than conventional regularizers like ridge regression, which zeroes in on parameter norms, this method shifts the regularization towards the parameters corresponding to the true labels, thus enhancing semantic consistency and model robustness.

Theoretical Implications

The paper explores the theoretical underpinnings of the proposed method, providing:

• Data-dependent Regularization Analysis: Through mathematical interpretations, the process of adversarial data augmentation is shown to introduce a form of regularization. The softmax-based classifiers benefit from an adaptation regularizer that aligns closer to true labels rather than centering purely on parameter magnitude.
• Adaptive Data Augmentation: The iterative adversarial examples can be described through the lens of Tikhonov regularized Newton-steps in the semantic space. This essentially means that each adversarially generated example represents a high-fidelity approximation of optimal perturbations needed to challenge the current model's decision boundary.

Empirical Results

The empirical evaluation validates the effectiveness of the proposed approach, primarily through two challenging tasks: digit classification and semantic scene segmentation.

• Digit Classification: Models were trained on the MNIST dataset and tested on several domain-shifted datasets including SVHN, MNIST-M, SYN, and USPS. Across various configurations, the proposed method exhibited consistent and significant improvements over standard empirical risk minimization (ERM) and other baseline regularization techniques like dropout and ridge regression.
• Semantic Scene Segmentation: Models trained on different weather and time conditions from the SYNTIA dataset demonstrated superior generalization capabilities when subsequently tested on unseen domain scenarios. This implies practical benefits for applications where models cannot be foreseen to every possible deployment scenario.

Future Implications and Developments

The research opens new avenues in the domain of unsupervised domain generalization. Despite its merits, selecting an appropriate value for the crucial hyperparameter $\gamma$ remains a challenging aspect. The paper addresses this by proposing an ensemble method, where models trained on different $\gamma$ values allow dynamic selection based on softmax confidence during inference.

Future work could extend these findings by developing more sophisticated heuristics for model selection at test time, particularly for complex tasks like semantic segmentation. Further theoretical explorations into the stability and convergence of these iterative adversarial methods could provide deeper insights into their applicability across different domains.

In summary, this paper presents a robust framework for enhancing model generalization capabilities to unseen domains through adversarial data augmentation. Its profound theoretical foundations and empirical validations make it an essential contribution to the field of machine learning, particularly in contexts where anticipatory domain adaptation is critical.